Evidence from environmental epidemiology research often contributes to the foundation of major policy decisions, driving policy makers to pose challenging questions to researchers. These questions are often best answered by using statistical methods that characterize the risk of a targeted environmental agent while taking other environmental variables into account. The nature and characteristics of environmental data and health outcomes make the risk estimation challenging and require the development of novel statistical methods. The purpose of this research is to develop models for integrated analyses of Spatio temporal data on exposure, health outcomes and covariates, incompletely observed and available at different levels of aggregation. Such models are needed for addressing a broad class of environmental agents that vary over time and across geographical regions. The focus is on the development of new statistical methods for: 1) estimating temporal associations between health outcomes and current and past environmental exposures, when the underlying function is unknown and exposure is measured with error (Aim A.1); 2) estimating spatial associations between health outcomes and environmental exposures which take proper account of non-random sampling designs (Aim A.2); and 3) conducting integrated analyses of spatio-temporal data on health and environmental exposures taking into account sources of bias arising from spatial and temporal aggregations (Aim A.3). We apply the proposed statistical methods to data on air pollution, mortality and temperature (Aim A.4). This research will provide a unified statistical framework for analyses of environmental epidemiological data of practical importance. The work proposed here will contribute statistical methodology to the field of environmental epidemiology, and will provide evidence on health effects of air pollution and temperature through the application of the proposed methods to various data sets available to us.